What kind of clients can use this MCP?

You can use this with any MCP-compatible client including Claude, Cursor, Windsurf, and VS Code.

Do I need to host the MCP myself?

No, Vinkius hosts and manages the MCP for you. You just connect and use it.

Can I use this to predict how long a job will take?

Yes, you can use the estimate_execution_time capability to project the duration of your processing pipeline.

How does it help with data partitioning?

The plan_workload_partitioning capability calculates how to distribute your records across different agents.

Is this for real-time data or batch processing?

This MCP is designed for modeling map-reduce workloads, which are typically used in batch processing and distributed computing.

How does this capability handle data skew?

The plan_workload_partitioning capability calculates a skew ratio. If the ratio exceeds 3, it flags the reducer as 'hot', indicating that you should consider salting keys to balance the load.

Can I estimate network costs?

Yes, by using calculate_shuffle_volume, you can determine the total bytes and megabytes that will be transferred during the shuffle phase.

What is the purpose of the execution time estimation?

The estimate_execution_time capability projects the total duration of the pipeline by summing the map phase and reduce phase durations based on your specific workloads.

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Connect Map-Reduce Partition Planner to ChatGPT and Claude

Set it up once and it works in ChatGPT, Claude, Cursor, and any other client you love. It all runs safely in the background, so you can relax and focus on what really matters. Get precise mathematical models for distributed computing workloads.

Ask AI about this Connector

Developed, maintained, and hosted by Vinkius.

MCP VERIFIED · PRODUCTION READY · VINKIUS GUARANTEED

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Works with modern AI clients that support MCP, including ChatGPT, Claude, Cursor, and more.

Your AI does the work. We keep it safe and sound.

No other AI tool gives you this. Your workflow runs in the client you already love, your account stays protected, and you decide what the agent can and cannot do. It is the whole reason to make the switch.

Each Connector lives inside our infrastructure, the same place where your workflow runs. The three things below are already part of it, working quietly in the background so you never have to think about them. Nothing to add, nothing to set up, nothing to worry about.

V8 Isolate01

Each Connector runs inside its own sealed environment.

Each Connector runs inside its own sealed V8 runtime — no access to your files, your system, or other servers. A Connector never sees past its own request.

Kill Switch02

One switch stops every agent. Instantly.

One action deactivates every Connector, revokes every token, and ends every session at once. AI Governance keeps the record.

FinOps per User03

Spending limits that pause instead of surprise.

Every user keeps their own connections and credentials, isolated from everyone else. Anything that would cross your spending limit is held for your approval, not billed.

Three controls, built into the layer your AI connects through.

Curated set · 3 capabilities

We ship only the actions that matter.

Capability set01 / 01

01-03

3 capabilities in this set.

Part of 3 available through Map-Reduce Partition Planner.

01
Calculate shuffle volume
This capability estimates the total data transfer requirements during the shuffle phase. It helps you understand the network load your pipeline will generate.
02
Estimate execution time
This capability predicts the total duration of your processing pipeline. It uses your specific workload parameters to project completion times.
03
Plan workload partitioning
This capability determines how to distribute records across your available agents. It ensures your data is split logically for efficient processing.

Real work

What teams actually run

Pipeline Capacity Planning

Use the MCP to decide how many agents you need to process a specific record count within a time limit.

Network Load Management

Calculate shuffle volumes to prevent network bottlenecks during large data transfers.

Job Scheduling

Predict execution times to better coordinate when distributed jobs start and end.

Resource Allocation

Determine the exact number of records per agent to ensure balanced workloads.

One connector, every AI

Map-Reduce Partition Planner works with the most popular AI clients.

These are the most popular clients, each with a step-by-step guide: one link, set up once, with governance and visibility built in. And because everything runs on the MCP standard, the same connection also works in any other compatible client — nothing to rebuild.

Building your own app? The connector is yours to use.

You don't need a client to put Map-Reduce Partition Planner to work: the same hosted connection plugs into your own applications and agent code, with the same governance on every request. Build with it, chat with it — one connection for both.

Observed, not estimated

839ms average. Fast in production.

Map-Reduce Partition Planner is checked daily against the live service.

Daily averagePeak 1036ms

Oct 3Today

Fastest day: 726ms
Slowest day: 1036ms
14-day trend: Slowing+19%

Connect your client

One URL. Every client.

Activate the Connector, copy your link, and paste it into the client you already use. 3 capabilities arrive ready to run.

Preview access · not provider authentication

The vk_preview_* token belongs to Vinkius preview infrastructure. It lets Claude discover and display the capabilities of Map-Reduce Partition Planner, so you can see the experience inside your AI.

It does not authenticate your account with Map-Reduce Partition Planner. Actions requiring credentials or live account data may not run until you activate the Connector and authorize the service.

Map-Reduce Partition Planner Connector

You're all set. Choose your MCP client and follow the setup instructions.

Connector linkhttps://edge.vinkius.com/vk_preview_x46GYvNVwmQnxgSmGYqCsESM9K8svqI6CYw0KF6I/mcp

Claude Desktop

Follow the steps below to connect in seconds.

1In Claude Desktop, open Settings → Connectors.
2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
3Click Add and start a new chat — Map-Reduce Partition Planner capabilities are ready to use.

Configuration · claude_desktop_config.jsonCopy

{
  "mcpServers": {
    "map-reduce-partition-planner-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_x46GYvNVwmQnxgSmGYqCsESM9K8svqI6CYw0KF6I/mcp"
    }
  }
}

Claude
ChatGPT
Cursor
VS Code
Windsurf
Claude Code
JetBrains
Cline

Step-by-step instructions for each client are in the guide. How to connect

Guided setup for Claude? Set up Map-Reduce Partition Planner in Claude and other AIs

See all the AI clients this connector works with ↑

Who it's for

Built for the work Map-Reduce Partition Planner owners hand off.

This MCP is built for engineers managing large scale distributed data systems.

01
Data Engineer
You use this to plan how data is split and moved through your pipelines.
02
Data Architect
You use the mathematical models to design more efficient distributed systems.
03
DevOps Engineer
You use execution time estimates to schedule workloads and manage resource availability.

FAQ

Questions Map-Reduce Partition Planner owners ask.

01
What kind of clients can use this MCP?
You can use this with any MCP-compatible client including Claude, Cursor, Windsurf, and VS Code.
02
Do I need to host the MCP myself?
No, Vinkius hosts and manages the MCP for you. You just connect and use it.
03
Can I use this to predict how long a job will take?
Yes, you can use the estimate_execution_time capability to project the duration of your processing pipeline.
04
How does it help with data partitioning?
The plan_workload_partitioning capability calculates how to distribute your records across different agents.
05
Is this for real-time data or batch processing?
This MCP is designed for modeling map-reduce workloads, which are typically used in batch processing and distributed computing.
06
How does this capability handle data skew?
The plan_workload_partitioning capability calculates a skew ratio. If the ratio exceeds 3, it flags the reducer as 'hot', indicating that you should consider salting keys to balance the load.
07
Can I estimate network costs?
Yes, by using calculate_shuffle_volume, you can determine the total bytes and megabytes that will be transferred during the shuffle phase.
08
What is the purpose of the execution time estimation?
The estimate_execution_time capability projects the total duration of the pipeline by summing the map phase and reduce phase durations based on your specific workloads.

Explore

More in Data Engineering

Connect Map-Reduce Partition Planner to Claude, Cursor, ChatGPT & more

Your AI does the work. We keep it safe and sound.

Each Connector runs inside its own sealed environment.

One switch stops every agent. Instantly.

Spending limits that pause instead of surprise.

We ship only the actions that matter.

01-03

Calculate shuffle volume

Estimate execution time

Plan workload partitioning

What teams actually run

Pipeline Capacity Planning

Network Load Management

Job Scheduling

Resource Allocation

Building your own app? The connector is yours to use.

Map-Reduce Partition Planner Connector

Claude Desktop

Data Engineer

Data Architect

DevOps Engineer

What kind of clients can use this MCP?

Do I need to host the MCP myself?

Can I use this to predict how long a job will take?

How does it help with data partitioning?

Is this for real-time data or batch processing?

How does this capability handle data skew?

Can I estimate network costs?

What is the purpose of the execution time estimation?

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